The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter
Open accessAbstract
Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitudinal data, we examine the impact of random effects distribution misspecification on a variety of inferences, including prediction, inference about covariate effects, prediction of random effects and estimation of random effects variances. We describe examples, theoretical calculations and simulations to elucidate situations in which the specification is and is not important. A key conclusion is the large degree of robustness of maximum likelihood for a wide variety of commonly encountered situations.
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Cites
Hulley, S. (1998) · JAMA · doi:10.1001/jama.280.7.605 · Cited by 6,216
Searle, S. R., Casella, G., & McCulloch, C. E. (1992) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316856 · Cited by 1,926
Self-citationNonparametric maximum likelihood estimation of a mixing distribution
Laird, N. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10480103 · Cited by 753
Metlay, J. P., Camargo, C. A., MacKenzie, T., McCulloch, C., Maselli, J., Levin, S. K., Kersey, A., & Gonzales, R. (2007) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2007.03.022 · Cited by 118
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Cited by
Robustness of linear mixed-effects models to violations of distributional assumptions
Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., Réale, D., Dochtermann, N. A., Garamszegi, L. Z., & Araya‐Ajoy, Y. G. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13434 · Cited by 1,393
“Unfortunately, very few studies have assessed random effect variances and predictions (e.g. Maas & Hox, 2004; McCulloch & Neuhaus, 2011; Verbeke & Lesaffre, 1996); for those who did, the estimation of the group variance is usually accurate, but random effect coefficients (best linear unbiased…”
BackgroundFixed and random effects models: making an informed choice
Bell, A., Fairbrother, M., & Jones, K. (2019) · Quality & Quantity · doi:10.1007/s11135-018-0802-x · Cited by 1,085
Intermediate and advanced topics in multilevel logistic regression analysis
Austin, P. C., & Merlo, J. (2017) · Statistics in Medicine · doi:10.1002/sim.7336 · Cited by 649
von Hippel, P. T., Workman, J., & Downey, D. B. (2018) · Sociology of Education · doi:10.1177/0038040718801760 · Cited by 214
Koopmans, R., Veit, S., & Yemane, R. (2019) · Ethnic and Racial Studies · doi:10.1080/01419870.2019.1654114 · Cited by 110
Metadata
- Authors
- McCulloch, C. E., & Neuhaus, J. M.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2011-08-01
- Volume
- 26
- Issue
- 3
- Pages
- 388-402
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2056825323
- Cited by
- 183
- References
- 4
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- green
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